Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit"O'Reilly Media, Inc.", 12 juni 2009 - 504 sidor This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.
This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
Innehåll
1 | |
39 | |
Chapter 3 Processing Raw Text | 79 |
Chapter 4 Writing Structured Programs | 129 |
Chapter 5 Categorizing and Tagging Words | 179 |
Chapter 6 Learning to Classify Text | 221 |
Chapter 7 Extracting Information from Text | 261 |
Chapter 8 Analyzing Sentence Structure | 291 |
Chapter 9 Building FeatureBased Grammars | 327 |
Chapter 10 Analyzing the Meaning of Sentences | 361 |
Chapter 11 Managing Linguistic Data | 407 |
The Language Challenge | 441 |
Bibliography | 449 |
459 | |
463 | |
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Natural Language Processing with Python Steven Bird,Ewan Klein,Edward Loper Ingen förhandsgranskning - 2009 |
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algorithm annotated assign bigram Brown Corpus called chapter characters chunk chunker classifier conditional frequency distribution contains context corpora create defined dictionary document encoding English entropy entry evaluate example feature extractor feature structures Figure first-order logic format FreqDist function genre grammar hypernym input label lexical lexicon linguistic look method module Monty Python n-gram naive Bayes naive Bayes classifier named entity Natural Language Processing NLTK nltk.corpus import node noun phrases object output pairs parameter parse parser part-of-speech tags patterns performance Python interpreter quantifier recursive recursive descent parser regular expression representation result Section semantic sentence sequence specify Steven Bird string synset syntactic Table tagset task test set the/DT tokens Toolbox training set Treebank tuples Unicode unigram unigram tagger variable verb WordNet write